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An improved ICM algorithm for remote sensing image segmentation |
Jun YANG1, Jianjie PEI2 |
1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China 2. Faculty of Geomatics, Lanzhou Jiaotong University & Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China; |
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Abstract The traditional iterated conditional model(ICM)algorithm, when applied to remote sensing image segmentation, is easy to show discrete patches and isolated points. In view of this phenomenon, an improved ICM remote sensing segmentation algorithm is proposed which is based on Markov random field(MRF). First, the robust bilateral filter(BF)which is efficient in preserving edges and denoising was merged and used for the preprocessing of the remote sensing image, and then the Otsu algorithm was applied to obtaining the initial clusters. The algorithm could overcome some problems that occurred in the traditional K-means algorithm such as the inability in determining the number of clusters, difficulty in controlling algorithm complexities, and appearance of overlapping in the segmented regions. Next, the MRF was used to describe the pixel spatial correlation forming ICM remote sensing image segmentation algorithm with contextual information. By using remote sensing image data validation, the approach proposed in this paper realizes more reliable segmentation results in comparison with the traditional ICM algorithm.
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Keywords
iterated conditional model(ICM)
Markov random field(MRF)
Otsu
bilateral filter(BF)
image segmentation
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Issue Date: 10 September 2018
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